Bibliographic record
Abstract
<p>This research reflects on the role of an urban planner in an increasingly connected, data-driven urban context. The rise of urban-focused digital platforms has positioned </p> <p>municipalities as key customers for profit-driven organizations selling data and analytical tools to enhance quality of life in cities. This research focuses on ten urban-focused platforms and assesses their market positioning (promised value) and real-life application (actualized value), uncovering benefits and challenges associated with their use. The analysis explores how platforms can influence planning decisions, and a planner’s role in</p> <p>influencing the use of platforms. Findings suggest that planners have a critical role to play in determining each platform’s ability to provide real value within each city’s unique context. Recommendations highlight considerations for planners, specifically: the limits of platform data, potential trade-offs, and alignment with current state operations. An acute understanding of these dynamics will support planners in navigating platform urbanism while upholding the public interest.</p> <p><br></p> <p>Key words: Platform Urbanism; civic technology; urban planning innovation.</p>
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".